OpenRaiser/Intern-Atlas huggingface dataset:
Approximation and Dependence via Multiteam Semantics
Venue: arXiv cs.LO 2015
Year: 2015

We define a variant of team semantics called multiteam semantics based on multisets and study the properties of various logics in this framework. In particular, we define natural probabilistic versions of inclusion and independence atoms and certain approximation operators motivated by approximate dependence atoms of Väänänen.
A note on coloring (even-hole,cap)-free graphs
Venue: arXiv cs.DM 2015
Year: 2015

A {\em hole} is a chordless cycle of length at least four. A hole is {\em even} (resp. {\em odd}) if it contains an even (resp. odd) number of vertices. A \emph{cap} is a graph induced by a hole with an additional vertex that is adjacent to exactly two adjacent vertices on the hole. In this note, we use a decomposition theorem by Conforti et al. (1999) to show that if a graph $G$ does not contain any even hole or cap as an induced subgraph, then $χ(G)\le \lfloor\frac{3}{2}ω(G)\rfloor$, where $χ(G)$ and $ω(G)$ are the chromatic number and the clique number of $G$, respectively. This bound is attained by odd holes and the Hajos graph. The proof leads to a polynomial-time $3/2$-approximation algorithm for coloring (even-hole,cap)-free graphs.
Generating Text with Deep Reinforcement Learning
Venue: arXiv cs.CL 2015
Year: 2015

We introduce a novel schema for sequence to sequence learning with a Deep Q-Network (DQN), which decodes the output sequence iteratively. The aim here is to enable the decoder to first tackle easier portions of the sequences, and then turn to cope with difficult parts. Specifically, in each iteration, an encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the input sequence, automatically create features to represent the internal states of and formulate a list of potential actions for the DQN. Take rephrasing a natural sentence as an example. This list can contain ranked potential words. Next, the DQN learns to make decision on which action (e.g., word) will be selected from the list to modify the current decoded sequence. The newly modified output sequence is subsequently used as the input to the DQN for the next decoding iteration. In each iteration, we also bias the reinforcement learning's attention to explore sequence portions which are previously difficult to be decoded. For evaluation, the proposed strategy was trained to decode ten thousands natural sentences. Our experiments indicate that, when compared to a left-to-right greedy beam search LSTM decoder, the proposed method performed competitively well when decoding sentences from the training set, but significantly outperformed the baseline when decoding unseen sentences, in terms of BLEU score obtained.
Learning Adversary Behavior in Security Games: A PAC Model Perspective
Venue: arXiv cs.AI 2015
Year: 2015

Recent applications of Stackelberg Security Games (SSG), from wildlife crime to urban crime, have employed machine learning tools to learn and predict adversary behavior using available data about defender-adversary interactions. Given these recent developments, this paper commits to an approach of directly learning the response function of the adversary. Using the PAC model, this paper lays a firm theoretical foundation for learning in SSGs (e.g., theoretically answer questions about the numbers of samples required to learn adversary behavior) and provides utility guarantees when the learned adversary model is used to plan the defender's strategy. The paper also aims to answer practical questions such as how much more data is needed to improve an adversary model's accuracy. Additionally, we explain a recently observed phenomenon that prediction accuracy of learned adversary behavior is not enough to discover the utility maximizing defender strategy. We provide four main contributions: (1) a PAC model of learning adversary response functions in SSGs; (2) PAC-model analysis of the learning of key, existing bounded rationality models in SSGs; (3) an entirely new approach to adversary modeling based on a non-parametric class of response functions with PAC-model analysis and (4) identification of conditions under which computing the best defender strategy against the learned adversary behavior is indeed the optimal strategy. Finally, we conduct experiments with real-world data from a national park in Uganda, showing the benefit of our new adversary modeling approach and verification of our PAC model predictions.
Some problems of Graph Based Codes for Belief Propagation decoding
Venue: arXiv cs.IT 2015
Year: 2015

Short survey about code on the graph by example of hardware friendly quasi-cycle LDPC code. We consider two main properties of code: weight enumerator (well known from classic code theory) and Trapping sets pseudocodewords weight spectrum (a subgraph in code graph's which become reasone of decoding failure under Belief propagation). In paper we show fast methods to measure first components of TS enumerator using EMD=ACE constrains for high girth codes on the graph using graph spectral classification method. This approach simplify solving trouble of agreed between minimal TS pseudocode weight and code distance (which can be measure using knowledge of Authomorphism for algebraic code design methods or measure using lattice reduction(LLL,BKZ) for random graph design methods). In the end of article author raise several challenge problems to improve Sparse and Dense parity-check codes decoding under Belief propagation.
I/O Efficient Core Graph Decomposition at Web Scale
Venue: arXiv cs.DB 2015
Year: 2015

Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. However, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works well in many web-scale graphs. We propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates, we show that our algorithm can be naturally extended to handle edge deletion. We also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties. We conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory.
Water Detection through Spatio-Temporal Invariant Descriptors
Venue: arXiv cs.CV 2015
Year: 2015

In this work, we aim to segment and detect water in videos. Water detection is beneficial for appllications such as video search, outdoor surveillance, and systems such as unmanned ground vehicles and unmanned aerial vehicles. The specific problem, however, is less discussed compared to general texture recognition. Here, we analyze several motion properties of water. First, we describe a video pre-processing step, to increase invariance against water reflections and water colours. Second, we investigate the temporal and spatial properties of water and derive corresponding local descriptors. The descriptors are used to locally classify the presence of water and a binary water detection mask is generated through spatio-temporal Markov Random Field regularization of the local classifications. Third, we introduce the Video Water Database, containing several hours of water and non-water videos, to validate our algorithm. Experimental evaluation on the Video Water Database and the DynTex database indicates the effectiveness of the proposed algorithm, outperforming multiple algorithms for dynamic texture recognition and material recognition by ca. 5% and 15% respectively.
A Formal Model for Direct-style Asynchronous Observables
Venue: arXiv cs.PL 2015
Year: 2015

Languages like F#, C#, and recently also Scala, provide "Async" programming models which aim to make asynchronous programming easier by avoiding an inversion of control that is inherent in callback-based programming models. This paper presents a novel approach to integrate the Async model with observable streams of the Reactive Extensions model. Reactive Extensions are best-known from the .NET platform, and widely-used implementations of its programming model exist also for Java, Ruby, and other languages. This paper contributes a formalization of the unified "Reactive Async" model in the context of an object-based core calculus. Our formal model captures the essence of the protocol of asynchronous observables using a heap evolution property. We prove a subject reduction theorem; the theorem implies that reduction preserves the heap evolution property. Thus, for well-typed programs our calculus ensures the protocol of asynchronous observables.
Spectrum Reservation Contract Design in TV White Space Networks
Venue: arXiv cs.GT 2015
Year: 2015

In this paper, we study a broker-based TV white space market, where unlicensed white space devices (WSDs) purchase white space spectrum from TV licensees via a third-party geo-location database (DB), which serves as a spectrum broker, reserving spectrum from TV licensees and then reselling the reserved spectrum to WSDs. We propose a contract-theoretic framework for the database's spectrum reservation under demand stochasticity and information asymmetry. In such a framework, the database offers a set of contract items in the form of reservation amount and the corresponding payment, and each WSD chooses the best contract item based on its private information. We systematically study the optimal reservation contract design (that maximizes the database's expected profit) under two different risk-bearing schemes: DB-bearing-risk and WSD-bearing-risk, depending on who (the database or the WSDs) will bear the risk of over reservation. Counter-intuitively, we show that the optimal contract under DB-bearing-risk leads to a higher profit for the database and a higher total network profit.
Overlapped List Successive Cancellation Approach for Hardware Efficient Polar Code Decoder
Venue: arXiv cs.IT 2015
Year: 2015

This paper presents an efficient hardware design approach for list successive cancellation (LSC) decoding of polar codes. By applying path-overlapping scheme, the l instances of (l > 1) successive cancellation (SC) decoder for LSC with list size l can be cut down to only one. This results in a dramatic reduction of the hardware complexity without any decoding performance loss. We also develop novel approaches to reduce the latencyassociated with the pipeline scheme. Simulation results show that with proposed design approach the hardware efficiency is increased significantly over the recently proposed LSC decoders.
Exposing the Hidden Web: An Analysis of Third-Party HTTP Requests on 1 Million Websites
Venue: arXiv cs.CR 2015
Year: 2015

This article provides a quantitative analysis of privacy-compromising mechanisms on 1 million popular websites. Findings indicate that nearly 9 in 10 websites leak user data to parties of which the user is likely unaware; more than 6 in 10 websites spawn third- party cookies; and more than 8 in 10 websites load Javascript code from external parties onto users' computers. Sites that leak user data contact an average of nine external domains, indicating that users may be tracked by multiple entities in tandem. By tracing the unintended disclosure of personal browsing histories on the Web, it is revealed that a handful of U.S. companies receive the vast bulk of user data. Finally, roughly 1 in 5 websites are potentially vulnerable to known National Security Agency spying techniques at the time of analysis.
Basis Collapse for Holographic Algorithms Over All Domain Sizes
Venue: arXiv cs.CC 2015
Year: 2015

The theory of holographic algorithms introduced by Valiant represents a novel approach to achieving polynomial-time algorithms for seemingly intractable counting problems via a reduction to counting planar perfect matchings and a linear change of basis. Two fundamental parameters in holographic algorithms are the \emph{domain size} and the \emph{basis size}. Roughly, the domain size is the range of colors involved in the counting problem at hand (e.g. counting graph $k$-colorings is a problem over domain size $k$), while the basis size $\ell$ captures the dimensionality of the representation of those colors. A major open problem has been: for a given $k$, what is the smallest $\ell$ for which any holographic algorithm for a problem over domain size $k$ "collapses to" (can be simulated by) a holographic algorithm with basis size $\ell$? Cai and Lu showed in 2008 that over domain size 2, basis size 1 suffices, opening the door to an extensive line of work on the structural theory of holographic algorithms over the Boolean domain. Cai and Fu later showed for signatures of full rank that over domain sizes 3 and 4, basis sizes 1 and 2, respectively, suffice, and they conjectured that over domain size $k$ there is a collapse to basis size $\lfloor\log_2 k\rfloor$. In this work, we resolve this conjecture in the affirmative for signatures of full rank for all $k$.
Approximating Subadditive Hadamard Functions on Implicit Matrices
Venue: arXiv cs.DS 2015
Year: 2015

An important challenge in the streaming model is to maintain small-space approximations of entrywise functions performed on a matrix that is generated by the outer product of two vectors given as a stream. In other works, streams typically define matrices in a standard way via a sequence of updates, as in the work of Woodruff (2014) and others. We describe the matrix formed by the outer product, and other matrices that do not fall into this category, as implicit matrices. As such, we consider the general problem of computing over such implicit matrices with Hadamard functions, which are functions applied entrywise on a matrix. In this paper, we apply this generalization to provide new techniques for identifying independence between two vectors in the streaming model. The previous state of the art algorithm of Braverman and Ostrovsky (2010) gave a $(1 \pm ε)$-approximation for the $L_1$ distance between the product and joint distributions, using space $O(\log^{1024}(nm) ε^{-1024})$, where $m$ is the length of the stream and $n$ denotes the size of the universe from which stream elements are drawn. Our general techniques include the $L_1$ distance as a special case, and we give an improved space bound of $O(\log^{12}(n) \log^{2}({nm \over ε})ε^{-7})$.
Finetuning Randomized Heuristic Search For 2D Path Planning: Finding The Best Input Parameters For R* Algorithm Through Series Of Experiments
Venue: arXiv cs.AI 2015
Year: 2015

Path planning is typically considered in Artificial Intelligence as a graph searching problem and R* is state-of-the-art algorithm tailored to solve it. The algorithm decomposes given path finding task into the series of subtasks each of which can be easily (in computational sense) solved by well-known methods (such as A*). Parameterized random choice is used to perform the decomposition and as a result R* performance largely depends on the choice of its input parameters. In our work we formulate a range of assumptions concerning possible upper and lower bounds of R* parameters, their interdependency and their influence on R* performance. Then we evaluate these assumptions by running a large number of experiments. As a result we formulate a set of heuristic rules which can be used to initialize the values of R* parameters in a way that leads to algorithm's best performance.
Data Language Specification via Terminal Attribution
Venue: arXiv cs.FL 2015
Year: 2015

Unstructured data have to be parsed in order to become usable. The complexity of grammar notations and the difficulty of grammar debugging limit the use of parsers for data preprocessing. We introduce a notation in which grammars are defined by simply dividing terminals into predefined classes and then splitting elements of some classes into multiple layered sub-groups. These LL(1) grammars are designed for data languages. They simplify the task of developing data parsers.
A web-based IDE for IDP
Venue: arXiv cs.PL 2015
Year: 2015

IDP is a knowledge base system based on first order logic. It is finding its way to a larger public but is still facing practical challenges. Adoption of new languages requires a newcomer-friendly way for users to interact with it. Both an online presence to try to convince potential users to download the system and offline availability to develop larger applications are essential. We developed an IDE which can serve both purposes through the use of web technology. It enables us to provide the user with a modern IDE with relatively little effort.
Visualising interactive inferences with IDPD3
Venue: arXiv cs.PL 2015
Year: 2015

A large part of the use of knowledge base systems is the interpretation of the output by the end-users and the interaction with these users. Even during the development process visualisations can be a great help to the developer. We created IDPD3 as a library to visualise models of logic theories. IDPD3 is a new version of $ID^{P}_{Draw}$ and adds support for visualised interactive simulations.
Understanding symmetries in deep networks
Venue: arXiv cs.LG 2015
Year: 2015

Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that a commonly used deep network, which uses convolution, batch normalization, reLU, max-pooling, and sub-sampling pipeline, possess more complex forms of symmetry arising from scaling-based reparameterization of the network weights. We propose to tackle the issue of the weight space symmetry by constraining the filters to lie on the unit-norm manifold. Consequently, training the network boils down to using stochastic gradient descent updates on the unit-norm manifold. Our empirical evidence based on the MNIST dataset shows that the proposed updates improve the test performance beyond what is achieved with batch normalization and without sacrificing the computational efficiency of the weight updates.
Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests
Venue: arXiv cs.LG 2015
Year: 2015

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.
Isogeometric approach for nonlinear bending and post-buckling analysis of functionally graded plates under thermal environment
Venue: arXiv cs.CE 2015
Year: 2015

In this paper, equilibrium and stability equations of functionally graded material (FGM) plate under thermal environment are formulated based on isogeometric analysis (IGA) in combination with higher-order shear deformation theory (HSDT). The FGM plate is made by a mixture of two distinct components, for which material properties not only vary continuously through thickness according to a power-law distribution but also are assumed to be a function of temperature. Temperature field is assumed to be constant in any plane and uniform, linear and nonlinear through plate thickness, respectively. The governing equation is in nonlinear form based on von Karman assumption and thermal effect. A NURBS-based isogeometric finite element formulation is utilized to naturally fulfil the rigorous C1-continuity required by the present plate model. Influences of gradient indices, boundary conditions, temperature distributions, material properties, length-to-thickness ratios on the behaviour of FGM plate are discussed in details. Numerical results demonstrate excellent performance of the present approach.
Mean-field inference of Hawkes point processes
Venue: arXiv cs.LG 2015
Year: 2015

We propose a fast and efficient estimation method that is able to accurately recover the parameters of a d-dimensional Hawkes point-process from a set of observations. We exploit a mean-field approximation that is valid when the fluctuations of the stochastic intensity are small. We show that this is notably the case in situations when interactions are sufficiently weak, when the dimension of the system is high or when the fluctuations are self-averaging due to the large number of past events they involve. In such a regime the estimation of a Hawkes process can be mapped on a least-squares problem for which we provide an analytic solution. Though this estimator is biased, we show that its precision can be comparable to the one of the Maximum Likelihood Estimator while its computation speed is shown to be improved considerably. We give a theoretical control on the accuracy of our new approach and illustrate its efficiency using synthetic datasets, in order to assess the statistical estimation error of the parameters.
Achieving Sensor Fusion for Collaborative Multi-level Monitoring of Pipeline Infrastructures
Venue: arXiv cs.SY 2015
Year: 2015

Large scale monitoring systems enable efficient field level data collection at high temporal and spatial resolutions. One example is the deployment of such systems in pipeline infrastructure applications which have to be monitored for leaks and protected from unauthorized access, with the potential of causing significant environmental and economic damage. The paper discusses a multi-level system architecture for data collection and processing based on the collaborative integration of wireless sensor networks and unmanned aerial vehicles. Three sensor fusion methods: Kalman filtering, Fuzzy Sensor Validation and Fusion (FUSVAF) and Consensus-based processing, are considered for intelligent data reduction and situational awareness while alleviating communication bottlenecks across the multi-level network. Simulation and experimental results are presented.